![]() ![]() Both elements are stored as 8-bit unsigned integers. ![]() We can see the data is a tuple (as we passed a tuple as argument to the from_tensor_slices() function), whereas the first element is in shape (28,28) while the second element is a scalar. TensorSpec(shape=(), dtype=tf.uint8, name=None)) This prints the dataset’s spec, as follows: Given we have the fashion MNIST data loaded, we can convert it into a tf.data dataset, like the following:ĭataset = tf._tensor_slices((train_image, train_label)) Plt.show() Creating a Dataset using tf.data The following is the complete code using generator function, which the output is same as the previous example: While in the above code, we provided the validation data as NumPy array, we can also use a generator instead and specify validation_steps argument. However, our generator function will emit batches indefinitely so we need to tell when an epoch is ended, using the steps_per_epoch argument to the fit() function. Keras can complete one epoch when the entire dataset is used once. When data are presented as NumPy array, we can tell how many samples are there by looking at the length of the array. Instead of providing the data and label, we just need to provide the generator as the generator will give out both. History = model.fit(batch_generator(train_image, train_label, 32),Įpochs=50, validation_data=(test_image, test_label), verbose=0) Training a Keras model with a generator is similar, using the fit() function: Once it reaches the end of the array, it will restart from the beginning. It will scan the input arrays in batches indefinitely. This function is supposed to be call with the syntax batch_generator(train_image, train_label, 32). A generator of the fashion MNIST dataset can be created as follows:ĭef batch_generator(image, label, batchsize): A generator function is the one with a yield statement to emit data while the function is running in parallel to the data consumer. The other way of training the same network is to provide the data from a Python generator function instead of a NumPy array. Running this code will print out the following:ģ13/313 – 0s 392us/step – loss: 0.5114 – sparse_categorical_accuracy: 0.8446Īnd also create the following plot of validation accuracy over the 50 epochs we trained our model: Print(model.evaluate(test_image, test_label)) Validation_data=(test_image, test_label), verbose=0) History = model.fit(train_image, train_label, (train_image, train_label), (test_image, test_label) = load_data() Then we can build a Keras model for classification, and with the model’s fit() function, we provide the NumPy array as data.įrom _mnist import load_dataįrom import Dense, Flattenįrom import Sequential An example is the fashion MNIST dataset that comes with the Keras API, which we have 60,000 training samples and 10,000 test samples of 28×28 pixels in grayscale and the corresponding classification label is encoded with integers 0 to 9. Training a Keras Model with NumPy Array and Generator FunctionĬreating a Dataest from Generator Functionĭata with Prefetch Training a Keras Model with NumPy Array and Generator Functionīefore we see how the tf.data API works, let’s review how we usually train a Keras model.įirst, we need a dataset. This article is split into four sections they are: A Gentle Introduction to tensorflow.data API ![]()
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